Abstract: The Gleason grade group (GG) is an important basis for assessing the malignancy of prostate cancer, but it requires invasive biopsy to obtain pathology. To noninvasively evaluate GG, an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning. First, a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area. Second, the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area. Finally, in the network training, a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed, which further improves the prediction performance of the network. Experimental results show that the proposed method is better than the traditional network model in predicting GG performance. The quadratic weighted Kappa is 0.471 1 and the positive predictive value for predicting clinically significant cancer is 0.936 9.

Key words: prostate cancer, Gleason grade groups (GGs), bi-parametric magnetic resonance imaging, deep learning, curriculum learning

摘要： 格里森分级组(Gleason grade group, GG)是评估前列腺癌恶性程度的重要依据，但需要侵入性活检才能获得病理结果。为了无创地预测GG，提出了一种基于课程学习训练的集成注意力模块的多尺度卷积神经网络的自动预测方法。首先，提出了基于感兴趣区域图像的病灶注意力图，并与残存注意力模块相融合，使网络更加关注病灶区域。其次，结合特征金字塔网络，使网络更好地学习病灶区域的多尺度信息。最后，在网络训练中，提出了基于视觉评价与病理分级一致性差异的课程学习网络训练框架，进一步提高了网络的预测性能。实验结果表明，该方法在预测GG性能方面优于传统网络模型。二次加权Kappa结果为0.4711，用于评估临床显著性癌症的阳性预测值为0.9369。

关键词: 前列腺癌，格里森级别组，双参数核磁共振图像，深度学习，课程学习